Participant info

Agency task: Agency decisions

Model: Agency decisions by VoC

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: stage_1_choice ~ age_z * voc_z * condition_trial + (voc_z * condition_trial || 
## Model:     subject_id)
## Data: agency_model_data
## Df full model: 12
##                        Effect df      Chisq p.value
## 1                       age_z  1       0.00    .965
## 2                       voc_z  1 166.62 ***   <.001
## 3             condition_trial  1       0.07    .796
## 4                 age_z:voc_z  1  12.28 ***   <.001
## 5       age_z:condition_trial  1       0.01    .941
## 6       voc_z:condition_trial  1  51.94 ***   <.001
## 7 age_z:voc_z:condition_trial  1     5.26 *    .022
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: stage_1_choice ~ age_z * voc_z * condition_trial + (1 + re1.voc_z +  
##     re1.condition_trial + re1.voc_z_by_condition_trial || subject_id)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##  38797.4  38902.4 -19386.7  38773.4    46747 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -37.884  -0.383   0.172   0.500  37.176 
## 
## Random effects:
##  Groups       Name                         Variance Std.Dev.
##  subject_id   (Intercept)                  4.51649  2.1252  
##  subject_id.1 re1.voc_z                    0.49714  0.7051  
##  subject_id.2 re1.condition_trial          0.82459  0.9081  
##  subject_id.3 re1.voc_z_by_condition_trial 0.04873  0.2207  
## Number of obs: 46759, groups:  subject_id, 150
## 
## Fixed effects:
##                              Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                  1.262274   0.175189   7.205 5.80e-13 ***
## age_z                        0.007817   0.175021   0.045 0.964374    
## voc_z                        1.094665   0.061063  17.927  < 2e-16 ***
## condition_trial             -0.020084   0.076998  -0.261 0.794221    
## age_z:voc_z                  0.218514   0.061010   3.582 0.000341 ***
## age_z:condition_trial       -0.005671   0.076827  -0.074 0.941160    
## voc_z:condition_trial        0.200551   0.025090   7.993 1.31e-15 ***
## age_z:voc_z:condition_trial  0.058322   0.025114   2.322 0.020219 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_z  voc_z  cndtn_ ag_z:v_ ag_z:c_ vc_z:_
## age_z       -0.001                                            
## voc_z        0.012  0.001                                     
## conditn_trl  0.011 -0.002  0.007                              
## age_z:voc_z  0.002  0.010 -0.006  0.001                       
## ag_z:cndtn_ -0.002  0.007  0.001 -0.009  0.004                
## vc_z:cndtn_  0.006  0.001  0.043  0.034 -0.001   0.005        
## ag_z:vc_z:_  0.001  0.003  0.000  0.005  0.036   0.033   0.000

Plot: Sensitivity to the value of choice

Plot: Sensitivity to value of choice with continuous age

Summary stats: Sensitivity to value of control

Agency task: Machine selection

Model: Optimal machine choices across trials by condition and age

## Mixed Model Anova Table (Type 3 tests, LRT-method)
## 
## Model: stage_2_acc ~ age_z * context * condition_trial + (context * 
## Model:     condition_trial || subject_id)
## Data: machine_model_data
## Df full model: 12
##                          Effect df     Chisq p.value
## 1                         age_z  1 15.49 ***   <.001
## 2                       context  1 29.27 ***   <.001
## 3               condition_trial  1 69.50 ***   <.001
## 4                 age_z:context  1      0.74    .389
## 5         age_z:condition_trial  1      0.70    .404
## 6       context:condition_trial  1    5.47 *    .019
## 7 age_z:context:condition_trial  1      1.71    .191
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: stage_2_acc ~ age_z * context * condition_trial + (1 + re1.context1 +  
##     re1.condition_trial + re1.context1_by_condition_trial ||      subject_id)
##    Data: data
## Control: glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+06))
## 
##      AIC      BIC   logLik deviance df.resid 
##  15084.2  15179.9  -7530.1  15060.2    21318 
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -10.7613   0.0972   0.2017   0.4001   2.4112 
## 
## Random effects:
##  Groups       Name                            Variance Std.Dev.
##  subject_id   (Intercept)                     1.7634   1.3279  
##  subject_id.1 re1.context1                    0.6000   0.7746  
##  subject_id.2 re1.condition_trial             0.2931   0.5414  
##  subject_id.3 re1.context1_by_condition_trial 0.1118   0.3344  
## Number of obs: 21330, groups:  subject_id, 149
## 
## Fixed effects:
##                                Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                     2.31548    0.11532  20.079  < 2e-16 ***
## age_z                           0.45937    0.11421   4.022 5.77e-05 ***
## context1                        0.40971    0.07119   5.755 8.65e-09 ***
## condition_trial                 0.49629    0.05390   9.208  < 2e-16 ***
## age_z:context1                 -0.06268    0.07112  -0.881   0.3782    
## age_z:condition_trial           0.04491    0.05342   0.841   0.4006    
## context1:condition_trial        0.09387    0.03923   2.393   0.0167 *  
## age_z:context1:condition_trial -0.05214    0.03913  -1.333   0.1827    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) age_z  cntxt1 cndtn_ ag_z:1 ag_z:_ cnt1:_
## age_z        0.038                                          
## context1     0.033  0.004                                   
## conditn_trl  0.077  0.011  0.031                            
## ag_z:cntxt1  0.000  0.023  0.054 -0.003                     
## ag_z:cndtn_  0.007  0.061 -0.002  0.071  0.021              
## cntxt1:cnd_  0.028  0.000  0.108  0.077  0.014  0.003       
## ag_z:cnt1:_ -0.003  0.017  0.014  0.004  0.106  0.066  0.106

Plot: Proportion optimal machine selections across age groups and trials

Explicit reward knowledge task

Explicit reward knowledge task: summary stats

Model: Explicit reward knowledge by age and true probabilities

## Mixed Model Anova Table (Type 3 tests, S-method)
## 
## Model: error ~ zTrueProb * zAge + (1 | subject_id)
## Data: explicitKnow.filtered
##           Effect        df         F p.value
## 1      zTrueProb 1, 748.00 23.42 ***   <.001
## 2           zAge 1, 148.00   7.59 **    .007
## 3 zTrueProb:zAge 1, 748.00      0.52    .473
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: error ~ zTrueProb * zAge + (1 | subject_id)
##    Data: data
## 
## REML criterion at convergence: 3093.5
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.5243 -0.7034 -0.1905  0.4417  4.1618 
## 
## Random effects:
##  Groups     Name        Variance Std.Dev.
##  subject_id (Intercept) 0.1159   0.3405  
##  Residual               1.6947   1.3018  
## Number of obs: 900, groups:  subject_id, 150
## 
## Fixed effects:
##                 Estimate Std. Error        df t value Pr(>|t|)    
## (Intercept)      1.58778    0.05154 148.00000  30.810  < 2e-16 ***
## zTrueProb       -0.21012    0.04342 747.99999  -4.840 1.58e-06 ***
## zAge            -0.14204    0.05156 148.00000  -2.755  0.00661 ** 
## zTrueProb:zAge  -0.03119    0.04344 747.99999  -0.718  0.47293    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) zTrPrb zAge 
## zTrueProb   0.000              
## zAge        0.000  0.000       
## zTruPrb:zAg 0.000  0.000  0.000

Plot: Explicit reward knowledge

---
title: "E2 VoC Analyses Part 2: Regression Analyses"
date: 1/8/24
output:
    html_document:
        df_print: 'paged'
        toc: true
        toc_float:
            collapsed: false
            smooth_scroll: true
        number_sections: false
        code_download: true
        self_contained: true
---

```{r chunk settings, include = FALSE}
# set chunk settings
knitr::opts_chunk$set(echo = FALSE, 
                      cache = TRUE,
                      message = FALSE,
                      warning = FALSE)
knitr::opts_chunk$set(dpi=600)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```

```{r load libraries, include = F}

#load libraries
library(tidyverse)
library(glue)
library(afex)

#load scripts
source('analysis_scripts/voc_functions.R')
```

```{r import data}

# read in learning data
learning_data <- read_csv('data/processed/learning_data.csv')

# read in participant ages
participant_ages <- read_csv('data/voc_sub_info.csv') 

# join
learning_data <- inner_join(learning_data, participant_ages, by = c('subject_id')) %>%
  mutate(age_group = case_when(age < 13 ~ 'Children',
                               age < 18 & age > 12.99 ~ 'Adolescents',
                               age > 18 ~ 'Adults'))

learning_data$age_group <- factor(learning_data$age_group,
                                  levels = c("Children", "Adolescents", "Adults"))

```

```{r process learning data}
learning_data <- learning_data %>%
  mutate(ev_choice = case_when(context == 0 ~ 9,
                               context == 1 ~ 7,
                               context == 2 ~ 5),
         ev_comp = 5 + offer,
         voc = ev_choice - ev_comp,
         better_machine = case_when(reward_prob_L > reward_prob_R ~ 1,
                                    reward_prob_L < reward_prob_R ~ 0,
         ),
         stage_2_acc = case_when(stage_2_choice == better_machine ~ 1,
                                 stage_2_choice != better_machine ~ 0)) %>%
  group_by(subject_id, context) %>%
  mutate(condition_trial = rank(trial),
         block = floor((trial-1)/21 + 1))

# exclude first-stage misses and first-stage RT < 150 ms
learning_data_filtered <- learning_data %>%
  filter(stage_1_rt > 150)

```

# Participant info
```{r subject information}
sub_info <- learning_data_filtered %>%
  ungroup() %>%
  select(subject_id, age, age_group, gender) %>%
  unique() %>%
  group_by(age_group) %>%
  summarize(N = n(), 
            min_age = min(age, na.rm = T),
            max_age = max(age, na.rm = T),
            mean_age = mean(age, na.rm = T),
            sd_age = sd(age, na.rm = T),
            n_female = sum(gender == 'Female'),
            n_male = sum(gender == 'Male'),
            n_other = sum(gender == 'Other'))
sub_info

```


# Agency task: Agency decisions 
## Model: Agency decisions by VoC
```{r agency model}
# select relevant variables 
agency_model_data <- learning_data_filtered %>%
  select(subject_id, stage_1_choice, voc, condition_trial, block, trial, age, age_group)

## REGRESSION MODEL ##
#z score continuous variables
agency_model_data$subject_id <- factor(agency_model_data$subject_id)
agency_model_data$voc_z <- scale_this(agency_model_data$voc)
agency_model_data$condition_trial <- scale_this(agency_model_data$condition_trial)
agency_model_data$age_z <- scale_this(agency_model_data$age)

#run model
agency_model <- mixed(stage_1_choice ~ age_z * voc_z * condition_trial + (voc_z * condition_trial || subject_id),
                      data = agency_model_data,
                      family = "binomial",
                      method = "LRT",
                      expand_re = T,
                      control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6)))

#show model results
agency_model
summary(agency_model)
```

## Plot: Sensitivity to the value of choice
```{r voc plot, fig.height = 4, fig.width = 7, unit = "in"}
## PLOT ##
agency_sub_means <- agency_model_data %>% 
  mutate(task_half = case_when(trial < 158 ~ "First Half of Task",
                              trial > 157 ~ "Second Half of Task")) %>%
  group_by(task_half, voc, subject_id, age_group) %>%
  summarize(mean_sub_agency = mean(stage_1_choice, na.rm = T))

agency_means <- agency_sub_means %>% 
  group_by(task_half, voc, age_group) %>%
  summarize(mean_agency = mean(mean_sub_agency, na.rm = T),
            se_agency = sd(mean_sub_agency / sqrt(n())))

agency_plot <- ggplot(agency_means, aes(x = voc, y = mean_agency, color = age_group)) +
  facet_wrap(~task_half) +
  geom_point(aes(color = age_group)) + 
  geom_errorbar(aes(ymin = mean_agency - se_agency, ymax = mean_agency + se_agency), width = .1) + 
  geom_line() +
  voc_theme() + 
  scale_color_manual(values=c("#84347C", "#B40424", "#EB6D1E"), name = "Age Group") +
  xlab("Value of Choice (VoC)") +
  ylab("Proportion Agency Choices") +
  geom_hline(yintercept = .5, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed")
agency_plot
```


## Plot: Sensitivity to value of choice with continuous age 
```{r voc plot continuous age, fig.height = 3.9, fig.width = 3, unit = "in"}

#run model without age to get random effects for each participant
agency_glmer <- mixed(stage_1_choice ~  voc_z * condition_trial + (voc_z * condition_trial | subject_id),
                      data = agency_model_data, 
                      family = binomial, 
                      method = "LRT",
                      control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=1e6)),
                      return = "merMod") 

#get fixed effect of zVoC
VoC_fixedeff <- as.data.frame(coef(summary(agency_glmer)))$Estimate[2]
VoC_int_fixedeff <- as.data.frame(coef(summary(agency_glmer)))$Estimate[4]

#get random effects
VoC_effects <- ranef(agency_glmer)$subject_id %>%
    rownames_to_column(var = "subject_id")

#combine with age
VoC_subEffects <- agency_model_data %>%
    select(subject_id, age) %>% 
    unique() %>%
    left_join(VoC_effects, by = c("subject_id")) %>%
    mutate(zVoCFull = voc_z + VoC_fixedeff, 
           intFull = `voc_z:condition_trial` + VoC_int_fixedeff)

#plot age by VoC effect
VoC_plot_continuousAge <- ggplot(VoC_subEffects, aes(x = age, y = zVoCFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC Effect") 
VoC_plot_continuousAge

#plot age by VoC x trial effect
VoC_plot_continuousAgeTrial <- ggplot(VoC_subEffects, aes(x = age, y = intFull)) +
    geom_point(color = "#EB6D1E") + 
    geom_smooth(method = "lm", color = "#84347C", fill = "#84347C") +
    voc_theme() + 
    xlab("Age") +
    ylab("VoC x Trial Effect") 
VoC_plot_continuousAgeTrial
```



## Summary stats: Sensitivity to value of control
```{r voc summary stats}

# What proportion of trials did participants choose agency when VoC was 0?
VoC_zero_means_sub <- learning_data_filtered %>% 
    filter(voc == 0) %>%
    group_by(subject_id, age_group) %>%
    summarize(meanSubAgency = mean(stage_1_choice, na.rm = T))

VoC_zero_means <- VoC_zero_means_sub %>%
  ungroup() %>%
  summarize(meanAgency = mean(meanSubAgency, na.rm = T),
              seAgency = sd(meanSubAgency / sqrt(n())))
VoC_zero_means
```



# Agency task: Machine selection
## Model: Optimal machine choices across trials by condition and age
```{r machine selection decisions}
# select variables for inclusion in mixed-effects model (no age for now)
machine_model_data <- learning_data_filtered %>%
  filter(stage_1_choice == 1) %>%
  filter(context < 2) %>%
  select(subject_id, stage_2_acc, context, condition_trial, block, age, age_group) %>%
  drop_na()

## REGRESSION MODEL ##
#z score continuous variables
machine_model_data$subject_id <- factor(machine_model_data$subject_id)
machine_model_data$context <- factor(machine_model_data$context)
machine_model_data$condition_trial <- scale_this(machine_model_data$condition_trial)
machine_model_data$age_z <- scale_this(machine_model_data$age)

#run model
machine_model <- mixed(stage_2_acc ~ age_z * context * condition_trial + (context * condition_trial || subject_id),
                      data = machine_model_data,
                      family = "binomial",
                      method = "LRT",
                      expand_re = T,
                      control=glmerControl(optimizer="bobyqa", optCtrl=list(maxfun=1e6)))

#show model results
machine_model
summary(machine_model)
```

## Plot: Proportion optimal machine selections across age groups and trials
```{r plot bandit choices across trials, width = 7, height = 4, unit = "in"}

## PLOT ##
machine_selection_sub_means <- machine_model_data %>%
  group_by(context, block, subject_id, age_group) %>% 
  summarize(sub_acc = mean(stage_2_acc, na.rm = T))

machine_selection_means <- machine_selection_sub_means %>%
  group_by(context, block, age_group) %>% 
  summarize(mean_acc = mean(sub_acc),
            se = sd(sub_acc)/sqrt(n()))

machine_selection_plot <- ggplot(machine_selection_means, aes(x=block, y=mean_acc, color=factor(context))) +
  facet_wrap(~age_group) +
  geom_point(size = 3) +
  geom_jitter(data = machine_selection_sub_means,  aes(x=block, y=sub_acc, color=factor(context)), size = .5) +
  geom_smooth(method = "lm", aes(fill = factor(context))) +
  geom_hline(yintercept = .5, linetype="dashed") +
  ylab("Proportion Optimal Machine Selections") +
  xlab("Block") +
  scale_x_continuous(breaks = c(4, 8, 12)) +
  scale_fill_manual(name="Context",
                    labels=c("90/10",
                             "70/30"),
                    values=c(color1, color3), 
                    guide = guide_legend(reverse=TRUE)) +
  scale_color_manual(name="Context",
                     labels=c("90/10",
                              "70/30"),
                     values=c(color1, color3),
                     guide = guide_legend(reverse=TRUE)) +
  voc_theme() +
  theme(strip.text = element_text(size=12))
machine_selection_plot
```



# Explicit reward knowledge task 
## Explicit reward knowledge task: summary stats
```{r explicit knowledge task}

# Read in data
explicitKnow <- read_csv('data/processed/explicit_data.csv') 
#explicitKnow$subject_id <- factor(explicitKnow$subject_id)

#combine with age
explicitKnow <- full_join(explicitKnow, participant_ages, by = c("subject_id"))

explicitKnow %>% 
  group_by(subject_id, age) %>% 
  summarize(m = mean(error)) %>% 
  ungroup() %>% 
  summarize(meanErr = mean(m, na.rm=T), sd = sd(m, na.rm = T))
```

## Model: Explicit reward knowledge by age and true probabilities
```{r explicit knowledge model}

#re-scale age and zTrueProb
explicitKnow.filtered <- explicitKnow %>%
    select(subject_id, age, true_prob, error) %>%
    drop_na()

explicitKnow.filtered$zAge <- scale(explicitKnow.filtered$age)
explicitKnow.filtered$zTrueProb <- scale(explicitKnow.filtered$true_prob)

# run model
explicitKnow_errorbyTrueProbAge.mixed <- mixed(error ~ zTrueProb*zAge + (1|subject_id), 
                                               data = explicitKnow.filtered,
                                               method = "S") 
explicitKnow_errorbyTrueProbAge.mixed
summary(explicitKnow_errorbyTrueProbAge.mixed)
```

## Plot: Explicit reward knowledge
```{r plot explicit knowledge}

explicitKnow <- explicitKnow %>%
  mutate(age_group = case_when(age < 13 ~ 'Children',
                               age < 18 & age > 12.99 ~ 'Adolescents',
                               age > 18 ~ 'Adults'))

explicitKnow$age_group <- factor(explicitKnow$age_group,
                                  levels = c("Children", "Adolescents", "Adults"))

# plot response by bandit
explicitKnow %>% drop_na() %>%
    ggplot(., aes(x=factor(true_prob), y=response, fill=age_group)) +
    geom_boxplot() +
    scale_fill_manual(values = c(color1, color2, color3), name = "Age Group") +
    ylab("Reported Reward Probability") +
    xlab("True Reward Probability") +
    scale_x_discrete(labels = c("10%", "30%", "50%", "70%", "90%")) +
    scale_y_continuous(breaks = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10), 
                     labels = c("10%", "20%", "30%", "40%", "50%", "60%", "70%", "80%", "90%", "100%")) +
    voc_theme()
```
